A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Performance Analysis of a Novel Dual-Frequency Multiple Access Relay Transmission Scheme

In this paper we present the performance analysis of a novel channel assignment scheme where two non-cooperative independent users simultaneously communicate with their destination through a single relay by using only two frequency channels. The analytic derivation of the probability of symbol error for two main relay techniques will be provided, namely Amplify-and-Forward (AF) and Decode-and-Forward (DF). As shown by the obtained results, our switched-frequency approach results in a model that can achieve full- diversity by means of maximum-likelihood decoding at the receiver. Our results are especially important in the DF case, since in traditional techniques (such as half-duplex two-time slot approaches) two sources si-multaneously transmit on the same channel through the first time slot, which necessitates some sort of su-perposition coding. However, since in our scheme both users transmit over orthogonal channels, such a coding scheme is not required. In addition, it is shown that the DF approach based on our novel channel assign-ment scheme outperforms the AF scheme, especially in scenarios where the relay is closer to the receiver.